






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class 				UpperCAmelCase_  (		__lowerCamelCase		):
     __lowerCamelCase      				=  (DEISMultistepScheduler,)
     __lowerCamelCase      				=  (('num_inference_steps', 25),)




     def   __UpperCAmelCase  (				self	,						**_lowerCAmelCase		):
          UpperCAmelCase__      :  Optional[Any]				      =			{
              """num_train_timesteps""": 1000,
              """beta_start""": 0.0_0_0_1,
              """beta_end""": 0.0_2,
              """beta_schedule""": """linear""",
              """solver_order""": 2,
          }

          config.update(**_lowerCAmelCase		)
          return config




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase=0	,						**_lowerCAmelCase		):
          UpperCAmelCase__      :  str				      =			dict(self.forward_default_kwargs		)
          UpperCAmelCase__      :  int				      =			kwargs.pop("""num_inference_steps"""	,						_lowerCAmelCase		)
          UpperCAmelCase__      :  Optional[int]				      =			self.dummy_sample
          UpperCAmelCase__      :  List[str]				      =			0.1 * sample
          UpperCAmelCase__      :  Union[str, Any]				      =			[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

          for scheduler_class in self.scheduler_classes:
               UpperCAmelCase__      :  List[Any]				      =			self.get_scheduler_config(**_lowerCAmelCase		)
               UpperCAmelCase__      :  List[Any]				      =			scheduler_class(**_lowerCAmelCase		)
               scheduler.set_timesteps(_lowerCAmelCase		)
               # copy over dummy past residuals
               UpperCAmelCase__      :  Optional[Any]				      =			dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(_lowerCAmelCase		)
                    UpperCAmelCase__      :  str				      =			scheduler_class.from_pretrained(_lowerCAmelCase		)
                    new_scheduler.set_timesteps(_lowerCAmelCase		)
                    # copy over dummy past residuals
                    UpperCAmelCase__      :  Any				      =			dummy_past_residuals[: new_scheduler.config.solver_order]

               UpperCAmelCase__      ,		UpperCAmelCase__      :  Optional[int]				      =			sample, sample
               for t in range(_lowerCAmelCase	,						time_step + scheduler.config.solver_order + 1		):
                    UpperCAmelCase__      :  str				      =			scheduler.step(_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						**_lowerCAmelCase		).prev_sample
                    UpperCAmelCase__      :  int				      =			new_scheduler.step(_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						**_lowerCAmelCase		).prev_sample

                    assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"




     def   __UpperCAmelCase  (				self		):
          pass




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase=0	,						**_lowerCAmelCase		):
          UpperCAmelCase__      :  int				      =			dict(self.forward_default_kwargs		)
          UpperCAmelCase__      :  str				      =			kwargs.pop("""num_inference_steps"""	,						_lowerCAmelCase		)
          UpperCAmelCase__      :  int				      =			self.dummy_sample
          UpperCAmelCase__      :  Tuple				      =			0.1 * sample
          UpperCAmelCase__      :  int				      =			[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

          for scheduler_class in self.scheduler_classes:
               UpperCAmelCase__      :  Union[str, Any]				      =			self.get_scheduler_config()
               UpperCAmelCase__      :  Union[str, Any]				      =			scheduler_class(**_lowerCAmelCase		)
               scheduler.set_timesteps(_lowerCAmelCase		)

               # copy over dummy past residuals (must be after setting timesteps)
               UpperCAmelCase__      :  int				      =			dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(_lowerCAmelCase		)
                    UpperCAmelCase__      :  Any				      =			scheduler_class.from_pretrained(_lowerCAmelCase		)
                    # copy over dummy past residuals
                    new_scheduler.set_timesteps(_lowerCAmelCase		)

                    # copy over dummy past residual (must be after setting timesteps)
                    UpperCAmelCase__      :  int				      =			dummy_past_residuals[: new_scheduler.config.solver_order]

               UpperCAmelCase__      :  Dict				      =			scheduler.step(_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						**_lowerCAmelCase		).prev_sample
               UpperCAmelCase__      :  int				      =			new_scheduler.step(_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						**_lowerCAmelCase		).prev_sample

               assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase=None	,						**_lowerCAmelCase		):
          if scheduler is None:
               UpperCAmelCase__      :  int				      =			self.scheduler_classes[0]
               UpperCAmelCase__      :  Optional[Any]				      =			self.get_scheduler_config(**_lowerCAmelCase		)
               UpperCAmelCase__      :  Union[str, Any]				      =			scheduler_class(**_lowerCAmelCase		)

          UpperCAmelCase__      :  Tuple				      =			self.scheduler_classes[0]
          UpperCAmelCase__      :  Any				      =			self.get_scheduler_config(**_lowerCAmelCase		)
          UpperCAmelCase__      :  Dict				      =			scheduler_class(**_lowerCAmelCase		)

          UpperCAmelCase__      :  Dict				      =			10
          UpperCAmelCase__      :  int				      =			self.dummy_model()
          UpperCAmelCase__      :  str				      =			self.dummy_sample_deter
          scheduler.set_timesteps(_lowerCAmelCase		)

          for i, t in enumerate(scheduler.timesteps		):
               UpperCAmelCase__      :  Optional[Any]				      =			model(_lowerCAmelCase	,						_lowerCAmelCase		)
               UpperCAmelCase__      :  Any				      =			scheduler.step(_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase		).prev_sample

          return sample




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Any				      =			dict(self.forward_default_kwargs		)

          UpperCAmelCase__      :  List[Any]				      =			kwargs.pop("""num_inference_steps"""	,						_lowerCAmelCase		)

          for scheduler_class in self.scheduler_classes:
               UpperCAmelCase__      :  Optional[int]				      =			self.get_scheduler_config()
               UpperCAmelCase__      :  Optional[int]				      =			scheduler_class(**_lowerCAmelCase		)

               UpperCAmelCase__      :  List[str]				      =			self.dummy_sample
               UpperCAmelCase__      :  int				      =			0.1 * sample

               if num_inference_steps is not None and hasattr(_lowerCAmelCase	,						"""set_timesteps"""		):
                    scheduler.set_timesteps(_lowerCAmelCase		)
               elif num_inference_steps is not None and not hasattr(_lowerCAmelCase	,						"""set_timesteps"""		):
                    UpperCAmelCase__      :  Any				      =			num_inference_steps

               # copy over dummy past residuals (must be done after set_timesteps)
               UpperCAmelCase__      :  Any				      =			[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
               UpperCAmelCase__      :  List[str]				      =			dummy_past_residuals[: scheduler.config.solver_order]

               UpperCAmelCase__      :  List[Any]				      =			scheduler.timesteps[5]
               UpperCAmelCase__      :  Union[str, Any]				      =			scheduler.timesteps[6]

               UpperCAmelCase__      :  List[str]				      =			scheduler.step(_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						**_lowerCAmelCase		).prev_sample
               UpperCAmelCase__      :  List[str]				      =			scheduler.step(_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase	,						**_lowerCAmelCase		).prev_sample

               self.assertEqual(output_a.shape	,						sample.shape		)
               self.assertEqual(output_a.shape	,						output_a.shape		)




     def   __UpperCAmelCase  (				self		):
          # make sure that iterating over schedulers with same config names gives same results
          # for defaults
          UpperCAmelCase__      :  Tuple				      =			DEISMultistepScheduler(**self.get_scheduler_config()		)
          UpperCAmelCase__      :  Dict				      =			self.full_loop(scheduler=_lowerCAmelCase		)
          UpperCAmelCase__      :  Dict				      =			torch.mean(torch.abs(_lowerCAmelCase		)		)

          assert abs(result_mean.item() - 0.2_3_9_1_6		) < 1e-3

          UpperCAmelCase__      :  Any				      =			DPMSolverSinglestepScheduler.from_config(scheduler.config		)
          UpperCAmelCase__      :  Any				      =			DPMSolverMultistepScheduler.from_config(scheduler.config		)
          UpperCAmelCase__      :  Optional[Any]				      =			UniPCMultistepScheduler.from_config(scheduler.config		)
          UpperCAmelCase__      :  Union[str, Any]				      =			DEISMultistepScheduler.from_config(scheduler.config		)

          UpperCAmelCase__      :  str				      =			self.full_loop(scheduler=_lowerCAmelCase		)
          UpperCAmelCase__      :  Union[str, Any]				      =			torch.mean(torch.abs(_lowerCAmelCase		)		)

          assert abs(result_mean.item() - 0.2_3_9_1_6		) < 1e-3




     def   __UpperCAmelCase  (				self		):
          for timesteps in [25, 50, 100, 999, 1000]:
               self.check_over_configs(num_train_timesteps=_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self		):
          self.check_over_configs(thresholding=_lowerCAmelCase		)
          for order in [1, 2, 3]:
               for solver_type in ["logrho"]:
                    for threshold in [0.5, 1.0, 2.0]:
                         for prediction_type in ["epsilon", "sample"]:
                              self.check_over_configs(
                                  thresholding=_lowerCAmelCase	,						prediction_type=_lowerCAmelCase	,						sample_max_value=_lowerCAmelCase	,						algorithm_type="""deis"""	,						solver_order=_lowerCAmelCase	,						solver_type=_lowerCAmelCase	,						)




     def   __UpperCAmelCase  (				self		):
          for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self		):
          for algorithm_type in ["deis"]:
               for solver_type in ["logrho"]:
                    for order in [1, 2, 3]:
                         for prediction_type in ["epsilon", "sample"]:
                              self.check_over_configs(
                                  solver_order=_lowerCAmelCase	,						solver_type=_lowerCAmelCase	,						prediction_type=_lowerCAmelCase	,						algorithm_type=_lowerCAmelCase	,						)
                              UpperCAmelCase__      :  Any				      =			self.full_loop(
                                  solver_order=_lowerCAmelCase	,						solver_type=_lowerCAmelCase	,						prediction_type=_lowerCAmelCase	,						algorithm_type=_lowerCAmelCase	,						)
                              assert not torch.isnan(_lowerCAmelCase		).any(), "Samples have nan numbers"




     def   __UpperCAmelCase  (				self		):
          self.check_over_configs(lower_order_final=_lowerCAmelCase		)
          self.check_over_configs(lower_order_final=_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self		):
          for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
               self.check_over_forward(num_inference_steps=_lowerCAmelCase	,						time_step=0		)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Union[str, Any]				      =			self.full_loop()
          UpperCAmelCase__      :  Optional[int]				      =			torch.mean(torch.abs(_lowerCAmelCase		)		)

          assert abs(result_mean.item() - 0.2_3_9_1_6		) < 1e-3




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  str				      =			self.full_loop(prediction_type="""v_prediction"""		)
          UpperCAmelCase__      :  Optional[int]				      =			torch.mean(torch.abs(_lowerCAmelCase		)		)

          assert abs(result_mean.item() - 0.0_9_1		) < 1e-3




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Union[str, Any]				      =			self.scheduler_classes[0]
          UpperCAmelCase__      :  Any				      =			self.get_scheduler_config(thresholding=_lowerCAmelCase	,						dynamic_thresholding_ratio=0		)
          UpperCAmelCase__      :  List[Any]				      =			scheduler_class(**_lowerCAmelCase		)

          UpperCAmelCase__      :  int				      =			10
          UpperCAmelCase__      :  str				      =			self.dummy_model()
          UpperCAmelCase__      :  Tuple				      =			self.dummy_sample_deter.half()
          scheduler.set_timesteps(_lowerCAmelCase		)

          for i, t in enumerate(scheduler.timesteps		):
               UpperCAmelCase__      :  Tuple				      =			model(_lowerCAmelCase	,						_lowerCAmelCase		)
               UpperCAmelCase__      :  Optional[Any]				      =			scheduler.step(_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase		).prev_sample

          assert sample.dtype == torch.floataa














'''simple docstring'''







# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


__lowerCAmelCase        =							"pytorch_model.bin"
__lowerCAmelCase        =							"pytorch_model.bin.index.json"
__lowerCAmelCase        =							"adapter_config.json"
__lowerCAmelCase        =							"adapter_model.bin"
__lowerCAmelCase        =							"adapter_model.safetensors"
__lowerCAmelCase        =							"tf_model.h5"
__lowerCAmelCase        =							"tf_model.h5.index.json"
__lowerCAmelCase        =							"model.ckpt"
__lowerCAmelCase        =							"flax_model.msgpack"
__lowerCAmelCase        =							"flax_model.msgpack.index.json"
__lowerCAmelCase        =							"model.safetensors"
__lowerCAmelCase        =							"model.safetensors.index.json"
__lowerCAmelCase        =							"config.json"
__lowerCAmelCase        =							"preprocessor_config.json"
__lowerCAmelCase        =							FEATURE_EXTRACTOR_NAME
__lowerCAmelCase        =							"generation_config.json"
__lowerCAmelCase        =							"modelcard.json"

__lowerCAmelCase        =							"▁"
__lowerCAmelCase        =							SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

__lowerCAmelCase        =							[
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
__lowerCAmelCase        =							[[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
__lowerCAmelCase        =							[[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]


def       __UpperCamelCase					(      lowercase_       :  Optional[int]   ):
       """simple docstring"""




       if version.parse(lowercase_   ) < version.parse(lowercase_   ):
              if "dev" in min_version:
                     a_						     =			(
                         'This example requires a source install from HuggingFace Transformers (see '
                         '`https://huggingface.co/docs/transformers/installation#install-from-source`),'
                     )
              else:
                     a_						     =			F'This example requires a minimum version of {min_version},'
              error_message += F' but the version found is {__version__}.\n'
              raise ImportError(
                  error_message
                  + 'Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other '
                  'versions of HuggingFace Transformers.'   )




